2  Tables

This document explains how to reproduce the tables presented in the paper.

3 Install Packages

We install the following packages using the groundhog package manager to increase computational reproducibility.

options(repos = c(CRAN = "https://cran.r-project.org")) 


if (!requireNamespace("groundhog", quietly = TRUE)) {
    install.packages("groundhog")
}

pkgs <- c("magrittr", "data.table", "stringr", "lubridate", "knitr", 
          "sandwich", "lmtest",
          "sjPlot", "stargazer", "gt")

groundhog::groundhog.library(pkg = pkgs,
                             date = "2024-08-01")

rm(pkgs)

3.1 Read Data

# data <- data.table::fread(file = "../data/processed/full.csv")
data <- readRDS(file="../data/processed/full.Rda")

3.2 Table 2

# Melt the data
long_df <- melt(data,
                id.vars = c("stage", "surprise", "communication", "participant.label"),
                measure.vars = c("b", "a", "E1", "E2", "E3", "E12", "E13", "E23"),
                variable.name = "Variable",
                value.name = "Value")

# Function to calculate mean and sd
calculate_stats <- function(x) {
  c(mean = mean(x, na.rm = TRUE), sd = sd(x, na.rm = TRUE))
}

# Calculate pooled summary
pooled_summary <- long_df[, as.list(calculate_stats(Value)), by = .(Variable, stage)]
setnames(pooled_summary, c("mean", "sd"), c("mean_pooled", "sd_pooled"))

# Calculate summary by treatment
summary_tmp <- long_df[, as.list(calculate_stats(Value)), 
                       by = .(surprise, communication, Variable, stage)]

# Reshape summary_tmp to wide format
summary_wide <- dcast(summary_tmp, 
                      Variable + stage ~ surprise + communication, 
                      value.var = c("mean", "sd"))

# Merge pooled summary with reshaped summary
summary_table_wide <- merge(x = pooled_summary,
                            y = summary_wide, 
                            by = c("Variable", "stage"))

# Function to format mean and sd without line break
format_mean_sd <- function(mean, sd) {
  sprintf("%.2f (%.2f)", mean, sd)
}

# Apply formatting
cols_to_format <- names(summary_table_wide)[3:length(names(summary_table_wide))]
summary_table_wide[, (cols_to_format) := lapply(.SD, as.numeric), .SDcols = cols_to_format]
summary_table_wide[, c("Pooled", "Confirmation_point", "Confirmation_both", "Confirmation_interval", 
                       "Contradiction_point", "Contradiction_both", "Contradiction_interval") := 
                     .(format_mean_sd(mean_pooled, sd_pooled),
                       format_mean_sd(mean_FALSE_point, sd_FALSE_point),
                       format_mean_sd(mean_FALSE_both, sd_FALSE_both),
                       format_mean_sd(mean_FALSE_interval, sd_FALSE_interval),
                       format_mean_sd(mean_TRUE_point, sd_TRUE_point),
                       format_mean_sd(mean_TRUE_both, sd_TRUE_both),
                       format_mean_sd(mean_TRUE_interval, sd_TRUE_interval))]

# Select only the formatted columns
summary_table_final <- summary_table_wide[, .(Variable, stage, Pooled, 
                                              Confirmation_point, Confirmation_both, Confirmation_interval,
                                              Contradiction_point, Contradiction_both, Contradiction_interval)]

# Calculate N for each group correctly
N_values <- data[, 
                 .(N = length(unique(participant.label))), 
                 by = c("communication", "surprise")]

# Reshape N_values to wide format
N_values_wide <- dcast(N_values, . ~ surprise + communication, value.var = "N")

# Calculate pooled N
N_pooled <- data[, length(unique(participant.label))]

# Create N row
N_row <- data.table(
  Variable = "Participants",
  stage = "",
  Pooled = as.character(N_pooled),
  Confirmation_point = as.character(N_values_wide$`FALSE_point`),
  Confirmation_both = as.character(N_values_wide$`FALSE_both`),
  Confirmation_interval = as.character(N_values_wide$`FALSE_interval`),
  Contradiction_point = as.character(N_values_wide$`TRUE_point`),
  Contradiction_both = as.character(N_values_wide$`TRUE_both`),
  Contradiction_interval = as.character(N_values_wide$`TRUE_interval`)
)

# Add N row to summary_table_final
summary_table_final <- rbindlist(list(summary_table_final, N_row), use.names = TRUE, fill = TRUE)

# Create gt table
summary_table_final %>% 
  gt(groupname_col = "Variable") %>% 
  cols_label(
    stage = "Stage",
    Pooled = "Pooled",
    Confirmation_point = "Point",
    Confirmation_both = "Point + interval",
    Confirmation_interval = "Interval",
    Contradiction_point = "Point",
    Contradiction_both = "Point + interval",
    Contradiction_interval = "Interval"
  ) %>%
  tab_spanner(
    label = "Confirmation",
    columns = c(Confirmation_point, Confirmation_both, Confirmation_interval)
  ) %>%
  tab_spanner(
    label = "Contradiction",
    columns = c(Contradiction_point, Contradiction_both, Contradiction_interval)
  ) %>%
  cols_align(
    align = "left",
    columns = c(Variable, stage)
  ) %>%
  cols_align(
    align = "center",
    columns = c(Pooled, Confirmation_point, Confirmation_both, Confirmation_interval,
                Contradiction_point, Contradiction_both, Contradiction_interval)
  ) %>%
  opt_row_striping() %>%
  tab_options(
    table.font.size = px(12),
    data_row.padding = px(4)
  ) %>%
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_body(
      columns = everything(),
      rows = Variable == "N"
    )
  )
Stage Pooled Confirmation Contradiction
Point Point + interval Interval Point Point + interval Interval
b
1 -0.07 (0.30) -0.05 (0.27) -0.09 (0.30) -0.07 (0.29) -0.04 (0.31) -0.08 (0.31) -0.08 (0.31)
2 -0.08 (0.32) -0.09 (0.31) -0.09 (0.32) -0.08 (0.32) -0.04 (0.31) -0.10 (0.34) -0.07 (0.34)
a
1 0.72 (0.49) 0.71 (0.50) 0.78 (0.47) 0.73 (0.51) 0.71 (0.49) 0.68 (0.44) 0.69 (0.51)
2 0.71 (0.54) 0.73 (0.54) 0.73 (0.51) 0.73 (0.56) 0.69 (0.54) 0.70 (0.53) 0.69 (0.56)
E1
1 47.41 (20.96) 46.65 (19.05) 49.08 (20.32) 47.98 (21.74) 45.16 (20.53) 46.65 (21.02) 48.93 (22.84)
2 50.19 (25.46) 47.61 (25.00) 46.58 (25.10) 46.35 (24.46) 52.63 (24.79) 56.24 (26.81) 51.57 (25.24)
E2
1 50.06 (20.80) 50.11 (19.95) 51.67 (20.49) 50.28 (21.62) 48.61 (20.08) 51.05 (20.78) 48.70 (21.84)
2 50.75 (23.38) 54.34 (22.15) 55.01 (23.46) 53.28 (24.48) 45.51 (21.29) 48.63 (24.14) 47.89 (23.23)
E3
1 48.43 (20.34) 46.28 (19.22) 51.09 (20.01) 48.48 (20.58) 47.51 (21.53) 48.47 (19.36) 48.83 (21.11)
2 46.70 (24.49) 48.44 (22.04) 49.31 (23.08) 48.47 (23.55) 42.09 (25.44) 45.72 (26.34) 46.27 (25.70)
E12
1 58.13 (20.00) 57.13 (19.64) 58.80 (19.76) 59.20 (19.23) 57.57 (20.24) 58.02 (21.17) 58.11 (20.00)
2 61.94 (24.17) 60.22 (22.64) 61.48 (21.98) 58.80 (23.39) 62.03 (26.61) 66.30 (25.07) 62.72 (24.54)
E13
1 55.60 (20.11) 55.41 (19.15) 55.89 (20.00) 54.06 (21.15) 52.73 (20.07) 57.07 (19.93) 58.31 (20.09)
2 55.93 (23.45) 54.08 (23.73) 52.53 (24.57) 54.12 (23.32) 56.94 (21.92) 59.55 (24.11) 58.19 (22.41)
E23
1 60.42 (22.37) 59.32 (23.18) 59.63 (22.24) 60.04 (22.48) 59.58 (23.51) 62.71 (21.38) 61.24 (21.38)
2 58.37 (24.38) 62.63 (24.44) 63.52 (23.92) 62.12 (23.02) 52.01 (24.71) 54.40 (24.54) 55.77 (23.29)
Participants
1505 255 247 243 252 250 258

3.3 Table 3

ols_3_1 <- lm(formula = b ~ age_35_52 + age_53_plus + female + high_education + high_income + 
    married + parentship, 
            data = data, 
            subset = (treated == FALSE))
se_3_1  <- coeftest(ols_3_1, vcov = vcovHC(ols_3_1, type = "HC1"))

ols_3_2 <- lm(formula = b ~ age_35_52 + age_53_plus + female + high_education + high_income + 
    married + parentship + high_temperature + high_usage + high_general_risk + 
    high_weather_risk + high_accuracy + high_credibility, 
            data = data, 
            subset = (treated == FALSE))
se_3_2  <- coeftest(ols_3_2, vcov = vcovHC(ols_3_2, type = "HC1"))

ols_3_3 <- lm(formula = a ~ age_35_52 + age_53_plus + female + high_education + high_income + 
    married + parentship, 
            data = data, 
            subset = (treated == FALSE))
se_3_3  <- coeftest(ols_3_3, vcov = vcovHC(ols_3_3, type = "HC1"))

ols_3_4 <- lm(formula = a ~ age_35_52 + age_53_plus + female + high_education + high_income + 
    married + parentship + high_temperature + high_usage + high_general_risk + 
    high_weather_risk + high_accuracy + high_credibility, 
            data = data, 
            subset = (treated == FALSE))
se_3_4  <- coeftest(ols_3_4, vcov = vcovHC(ols_3_4, type = "HC1"))
se_3 <- list(se_3_1[,2], se_3_2[,2], se_3_3[,2], se_3_4[,2])
p_3  <- list(se_3_1[,4], se_3_2[,4], se_3_3[,4], se_3_4[,4])

stargazer(ols_3_1, ols_3_2, ols_3_3, ols_3_4, 
          align = TRUE, 
          se = se_3, 
          p = p_3,   
          title = "Linear regressions: Explanatory analysis of Ambiguity Indices b and a",
          covariate.labels = c("age(35-52)",
                               "age(53-69)",
                               "gender (female)",
                               "high education",
                               "high income",
                               "family (married or same sex union)",
                               "parentship",
                               "high temperature (median)",
                               "high weather forecast usage (median)",
                               "high general risk attitude (median)",
                               "high weather risk attitude (median)",
                               "high accuracy (median)",
                               "high credibility (median)",
                               "Constant"), 
          font.size = "scriptsize",
          type = "html", 
          df = FALSE,
          notes = c("The underlying standard errors (“HC1”, in parentheses) are clustered at the individual level and estimated with the R package sandwich (Zeileis 2004; Zeileis et al. 2020)"),
          notes.align = "l")
Linear regressions: Explanatory analysis of Ambiguity Indices b and a
Dependent variable:
b a
(1) (2) (3) (4)
age(35-52) -0.004 -0.001 0.065* 0.050
(0.021) (0.021) (0.037) (0.036)
age(53-69) -0.047** -0.043* 0.149*** 0.139***
(0.023) (0.023) (0.038) (0.037)
gender (female) -0.064*** -0.055*** 0.037 0.040
(0.017) (0.017) (0.027) (0.027)
high education -0.071*** -0.072*** 0.022 0.025
(0.018) (0.018) (0.028) (0.028)
high income 0.024 0.019 0.018 0.027
(0.017) (0.017) (0.030) (0.031)
family (married or same sex union) -0.019 -0.017 -0.056* -0.059*
(0.019) (0.019) (0.032) (0.033)
parentship 0.020 0.016 -0.054* -0.052*
(0.020) (0.020) (0.030) (0.030)
high temperature (median) -0.022 -0.074***
(0.016) (0.027)
high weather forecast usage (median) -0.016 -0.008
(0.016) (0.026)
high general risk attitude (median) 0.028 -0.010
(0.018) (0.027)
high weather risk attitude (median) 0.025 -0.028
(0.018) (0.028)
high accuracy (median) -0.025 -0.010
(0.022) (0.035)
high credibility (median) 0.007 -0.046
(0.017) (0.029)
Constant 0.013 0.017 0.656*** 0.759***
(0.023) (0.034) (0.039) (0.053)
Observations 1,361 1,361 1,361 1,361
R2 0.028 0.036 0.015 0.024
Adjusted R2 0.023 0.026 0.009 0.015
Residual Std. Error 0.289 0.289 0.480 0.479
F Statistic 5.550*** 3.835*** 2.845*** 2.542***
Note: p<0.1; p<0.05; p<0.01
The underlying standard errors (“HC1”, in parentheses) are clustered at the individual level and estimated with the R package sandwich (Zeileis 2004; Zeileis et al. 2020)

3.4 Table 4

ols_4_1 <- lm(formula = b ~ surprise + treated + surprise*treated, 
            data = data)
se_4_1  <- coeftest(x = ols_4_1, 
                    vcov = vcovCL(ols_4_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))

ols_4_2 <- lm(formula = b ~ communication + treated + communication*treated, 
            data = data,
            subset = (surprise == FALSE))
se_4_2  <- coeftest(x = ols_4_2, 
                    vcov = vcovCL(ols_4_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))

ols_4_3 <- lm(formula = b ~ communication + treated + communication*treated, 
            data = data,
            subset = (surprise == TRUE))
se_4_3  <- coeftest(x = ols_4_3, 
                    vcov = vcovCL(ols_4_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))

ols_4_4 <- lm(formula = b ~ surprise + treated + surprise*treated, 
            data = data,
            subset = (communication == "point"))
se_4_4  <- coeftest(x = ols_4_4, 
                    vcov = vcovCL(ols_4_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))

ols_4_5 <- lm(formula = b ~ surprise + treated + surprise*treated, 
            data = data,
            subset = (communication == "interval"))
se_4_5  <- coeftest(x = ols_4_5, 
                    vcov = vcovCL(ols_4_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))

ols_4_6 <- lm(formula = b ~ surprise + treated + surprise*treated, 
            data = data,
            subset = (communication == "both"))
se_4_6  <- coeftest(x = ols_4_6, 
                    vcov = vcovCL(ols_4_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
se_4 <- list(se_4_1[,2], se_4_2[,2], se_4_3[,2], se_4_4[,2], se_4_5[,2], se_4_6[,2])
p_4  <- list(se_4_1[,4], se_4_2[,4], se_4_3[,4], se_4_4[,4], se_4_5[,4], se_4_6[,4])

stargazer(ols_4_1, ols_4_2, ols_4_3, ols_4_4, ols_4_5, ols_4_6, 
          align = TRUE, 
          se = se_4, 
          p = p_4,   
          title = "Linear regressions: Treatment effects on ambiguity index b",
          dep.var.caption = "Dependent variable: b",
          dep.var.labels = " ",
          model.names = FALSE,
          column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
          covariate.labels = c("contradiction", "both", "interval", "stage 2", "contradiction x stage 2", "both x stage 2", "interval x stage 2", "Constant"),
          font.size = "scriptsize",
          type = "html", 
          df = FALSE,
          notes = c("The underlying standard errors (“HC1”, in parentheses) are clustered at the individual level and estimated with the R package sandwich (Zeileis 2004; Zeileis et al. 2020)"),
          notes.align = "l")
Linear regressions: Treatment effects on ambiguity index b
Dependent variable: b
full confirmation contradiction point interval both
(1) (2) (3) (4) (5) (6)
contradiction 0.002 0.012 -0.014 0.007
(0.015) (0.026) (0.027) (0.027)
both -0.038 -0.043
(0.025) (0.027)
interval -0.017 -0.043
(0.025) (0.027)
stage 2 -0.020** -0.041*** -0.0002 -0.041*** -0.010 -0.008
(0.008) (0.014) (0.014) (0.014) (0.014) (0.015)
contradiction x stage 2 0.014 0.041** 0.016 -0.015
(0.012) (0.020) (0.020) (0.020)
both x stage 2 0.034 -0.023
(0.021) (0.020)
interval x stage 2 0.031 0.006
(0.020) (0.020)
Constant -0.068*** -0.050*** -0.037* -0.050*** -0.067*** -0.087***
(0.010) (0.017) (0.019) (0.017) (0.019) (0.019)
Observations 3,010 1,490 1,520 1,014 1,002 994
R2 0.001 0.003 0.006 0.005 0.0003 0.001
Adjusted R2 -0.0002 -0.001 0.002 0.003 -0.003 -0.002
Residual Std. Error 0.310 0.300 0.320 0.299 0.317 0.315
F Statistic 0.770 0.819 1.694 1.851 0.083 0.244
Note: p<0.1; p<0.05; p<0.01
The underlying standard errors (“HC1”, in parentheses) are clustered at the individual level and estimated with the R package sandwich (Zeileis 2004; Zeileis et al. 2020)

3.5 Table 5

ols_5_1 <- lm(formula = a ~ surprise + treated + surprise*treated,
              data = data)
se_5_1  <- coeftest(x = ols_5_1, 
                    vcov = vcovCL(ols_5_1,
                                  cluster = ~data$participant.label,
                                  type = "HC1"))

ols_5_2 <- lm(formula = a ~ communication + treated + communication*treated,
              data = data,
              subset = (surprise == FALSE))
se_5_2  <- coeftest(x = ols_5_2, 
                    vcov = vcovCL(ols_5_2,
                                  cluster = data[surprise == FALSE, participant.label],
                                  type = "HC1"))

ols_5_3 <- lm(formula = a ~ communication + treated + communication*treated,
              data = data,
              subset = (surprise == TRUE))
se_5_3  <- coeftest(x = ols_5_3, 
                    vcov = vcovCL(ols_5_3,
                                  cluster = data[surprise == TRUE, participant.label],
                                  type = "HC1"))

ols_5_4 <- lm(formula = a ~ surprise + treated + surprise*treated, 
              data = data,
              subset = (communication == "point"))
se_5_4  <- coeftest(x = ols_5_4, 
                    vcov = vcovCL(ols_5_4,
                                  cluster = data[communication == "point", participant.label],
                                  type = "HC1"))

ols_5_5 <- lm(formula = a ~ surprise + treated + surprise*treated, 
              data = data,
              subset = (communication == "interval"))
se_5_5  <- coeftest(x = ols_5_5, 
                    vcov = vcovCL(ols_5_5,
                                  cluster = data[communication == "interval", participant.label],
                                  type = "HC1"))

ols_5_6 <- lm(formula = a ~ surprise + treated + surprise*treated, 
              data = data,
              subset = (communication == "both"))
se_5_6  <- coeftest(x = ols_5_6, 
                    vcov = vcovCL(ols_5_6,
                                  cluster = data[communication == "both", participant.label],
                                  type = "HC1"))
se_5 <- list(se_5_1[,2], se_5_2[,2], se_5_3[,2], se_5_4[,2], se_5_5[,2], se_5_6[,2])
p_5  <- list(se_5_1[,4], se_5_2[,4], se_5_3[,4], se_5_4[,4], se_5_5[,4], se_5_6[,4])

stargazer(ols_5_1, ols_5_2, ols_5_3, ols_5_4, ols_5_5, ols_5_6, 
          align = TRUE, 
          se = se_5, 
          p = p_5,   
          title = "Linear regressions: Treatment effects on ambiguity index a",
          dep.var.caption = "Dependent variable: a",
          dep.var.labels = " ",
          model.names = FALSE,
          column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
          covariate.labels = c("contradiction", "both", "interval", "stage 2", "contradiction x stage 2", "both x stage 2", "interval x stage 2", "Constant"),
          font.size = "scriptsize",
          type = "html", 
          df = FALSE,
          notes = c("The underlying standard errors (“HC1”, in parentheses) are clustered at the individual level and estimated with the R package sandwich (Zeileis 2004; Zeileis et al. 2020)"),
          notes.align = "l")
Linear regressions: Treatment effects on ambiguity index a
Dependent variable: a
full confirmation contradiction point interval both
(1) (2) (3) (4) (5) (6)
contradiction -0.045* 0.002 -0.046 -0.092**
(0.025) (0.044) (0.045) (0.041)
both 0.063 -0.030
(0.043) (0.041)
interval 0.023 -0.026
(0.045) (0.044)
stage 2 -0.007 0.023 -0.022 0.023 -0.004 -0.041
(0.020) (0.036) (0.033) (0.036) (0.033) (0.033)
contradiction x stage 2 0.007 -0.044 0.006 0.061
(0.027) (0.049) (0.046) (0.046)
both x stage 2 -0.064 0.042
(0.049) (0.046)
interval x stage 2 -0.027 0.024
(0.049) (0.046)
Constant 0.740*** 0.712*** 0.714*** 0.712*** 0.734*** 0.775***
(0.018) (0.031) (0.031) (0.031) (0.033) (0.030)
Observations 3,010 1,490 1,520 1,014 1,002 994
R2 0.002 0.001 0.0004 0.001 0.002 0.005
Adjusted R2 0.001 -0.002 -0.003 -0.002 -0.001 0.002
Residual Std. Error 0.513 0.516 0.512 0.515 0.535 0.490
F Statistic 1.647 0.405 0.123 0.285 0.549 1.639
Note: p<0.1; p<0.05; p<0.01
The underlying standard errors (“HC1”, in parentheses) are clustered at the individual level and estimated with the R package sandwich (Zeileis 2004; Zeileis et al. 2020)

3.6 Table 6

ols_6_1 <- lm(formula = ed ~ surprise, 
            data = data,
            subset = (stage == 2))
se_6_1  <- coeftest(ols_6_1, vcov = vcovHC(ols_6_1, type = "HC1"))

ols_6_2 <- lm(formula = ed ~ communication, 
            data = data,
            subset = (stage == 2 & surprise == FALSE))
se_6_2  <- coeftest(ols_6_2, vcov = vcovHC(ols_6_2, type = "HC1"))

ols_6_3 <- lm(formula = ed ~ communication, 
            data = data,
            subset = (stage == 2 & surprise == TRUE))
se_6_3  <- coeftest(ols_6_3, vcov = vcovHC(ols_6_3, type = "HC1"))

ols_6_4 <- lm(formula = ed ~ surprise, 
            data = data,
            subset = (stage == 2 & communication == "point"))
se_6_4  <- coeftest(ols_6_4, vcov = vcovHC(ols_6_4, type = "HC1"))

ols_6_5 <- lm(formula = ed ~ surprise, 
            data = data,
            subset = (stage == 2 & communication == "interval"))
se_6_5  <- coeftest(ols_6_5, vcov = vcovHC(ols_6_5, type = "HC1"))

ols_6_6 <- lm(formula = ed ~ surprise, 
            data = data,
            subset = (stage == 2 & communication == "both"))
se_6_6  <- coeftest(ols_6_6, vcov = vcovHC(ols_6_6, type = "HC1"))
se_6 <- list(se_6_1[,2], se_6_2[,2], se_6_3[,2], se_6_4[,2], se_6_5[,2], se_6_6[,2])
p_6  <- list(se_6_1[,4], se_6_2[,4], se_6_3[,4], se_6_4[,4], se_6_5[,4], se_6_6[,4])

stargazer(ols_6_1, ols_6_2, ols_6_3, ols_6_4, ols_6_5, ols_6_6, 
          align = TRUE, 
          se = se_6,
          p = p_6,   
          title = "Linear regressions: Treatment effects on Euclidian distance between vector of matching probabilities in stage 1 vs. stage 2",
          dep.var.caption = "Dependent variable: Euclidian distance stage 2 vs. stage 1",
          dep.var.labels = " ",
          model.names = FALSE,
          column.labels = c("full", "confirmation", "contradiction", "point", "interval", "both"),
          covariate.labels = c("contradiction", "both", "interval", "Constant"),
          font.size = "scriptsize",
          type = "html", 
          df = FALSE,
          notes = c("Heteroscedasticity consistent standard errors (“HC1”, in parentheses) in parentheses and estimated with the R package sandwich (Zeileis 2004; Zeileis et al. 2020)"),
          notes.align = "l")
Linear regressions: Treatment effects on Euclidian distance between vector of matching probabilities in stage 1 vs. stage 2
Dependent variable: Euclidian distance stage 2 vs. stage 1
full confirmation contradiction point interval both
(1) (2) (3) (4) (5) (6)
contradiction 6.824*** 10.120*** 2.423 8.023**
(1.775) (3.029) (3.033) (3.158)
both 1.195 -0.902
(2.991) (3.194)
interval 0.701 -6.996**
(2.933) (3.126)
Constant 44.941*** 44.316*** 54.436*** 44.316*** 45.017*** 45.511***
(1.224) (2.001) (2.274) (2.001) (2.144) (2.223)
Observations 1,505 745 760 507 501 497
R2 0.010 0.0002 0.008 0.022 0.001 0.013
Adjusted R2 0.009 -0.002 0.005 0.020 -0.001 0.011
Residual Std. Error 34.441 33.445 35.337 34.076 33.956 35.205
F Statistic 14.768*** 0.081 2.966* 11.180*** 0.637 6.453**
Note: p<0.1; p<0.05; p<0.01
Heteroscedasticity consistent standard errors (“HC1”, in parentheses) in parentheses and estimated with the R package sandwich (Zeileis 2004; Zeileis et al. 2020)

Session Info

sessionInfo()
R version 4.4.1 (2024-06-14)
Platform: x86_64-apple-darwin20
Running under: macOS Sonoma 14.4.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Europe/Zurich
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] gt_0.11.0         stargazer_5.2.3   sjPlot_2.8.16     lmtest_0.9-40    
 [5] zoo_1.8-12        sandwich_3.1-0    knitr_1.48        lubridate_1.9.3  
 [9] stringr_1.5.1     data.table_1.15.4 magrittr_2.0.3   

loaded via a namespace (and not attached):
 [1] sass_0.4.9         tidyr_1.3.1        utf8_1.2.4         generics_0.1.3    
 [5] xml2_1.3.6         stringi_1.8.4      lattice_0.22-6     digest_0.6.36     
 [9] evaluate_0.24.0    grid_4.4.1         timechange_0.3.0   fastmap_1.2.0     
[13] jsonlite_1.8.8     ggeffects_1.7.0    groundhog_3.2.0    purrr_1.0.2       
[17] fansi_1.0.6        scales_1.3.0       cli_3.6.3          sjmisc_2.8.10     
[21] rlang_1.1.4        performance_0.12.2 munsell_0.5.1      withr_3.0.1       
[25] yaml_2.3.10        datawizard_0.12.2  sjstats_0.19.0     tools_4.4.1       
[29] parallel_4.4.1     dplyr_1.1.4        colorspace_2.1-1   ggplot2_3.5.1     
[33] sjlabelled_1.2.0   vctrs_0.6.5        R6_2.5.1           lifecycle_1.0.4   
[37] htmlwidgets_1.6.4  insight_0.20.2     pkgconfig_2.0.3    pillar_1.9.0      
[41] gtable_0.3.5       glue_1.7.0         xfun_0.46          tibble_3.2.1      
[45] tidyselect_1.2.1   rstudioapi_0.16.0  htmltools_0.5.8.1  rmarkdown_2.27    
[49] compiler_4.4.1